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https://github.com/AstraBert/everything-ai

Your fully proficient, AI-powered and local chatbot assistant🤖
https://github.com/AstraBert/everything-ai

ai ai-studio chatgpt cheshire-cat docker-image free-to-use generative-ai image-classification jan-ai langchain learning local-llm open-source qdrant stable-diffusion text-summarization

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Your fully proficient, AI-powered and local chatbot assistant🤖

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everything-ai


Your fully proficient, AI-powered and local chatbot assistant🤖





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        Flowchart

        
Flowchart for everything-ai


   





## Quickstart

### 1. Clone this repository

```bash

git clone https://github.com/AstraBert/everything-ai.git

cd everything-ai

```

### 2. Set your `.env` file

Modify:

- `VOLUME` variable in the .env file so that you can mount your local file system into Docker container.

- `MODELS_PATH` variable in the .env file so that you can tell llama.cpp where you stored the GGUF models you downloaded.

- `MODEL` variable in the .env file so that you can tell llama.cpp what model to use (use the actual name of the gguf file, and do not forget the .gguf extension!)

- `MAX_TOKENS` variable in the .env file so that you can tell llama.cpp how many new tokens it can generate as output.



An example of a `.env` file could be:

```bash

VOLUME="c:/Users/User/:/User/"

MODELS_PATH="c:/Users/User/.cache/llama.cpp/"

MODEL="stories260K.gguf"

MAX_TOKENS="512"

```

This means that now everything that is under "c:/Users/User/" on your local machine is under "/User/" in your Docker container, that llama.cpp knows where to look for models and what model to look for, along with the maximum new tokens for its output.



### 3. Pull the necessary images

```bash

docker pull astrabert/everything-ai:latest

docker pull qdrant/qdrant:latest

docker pull ghcr.io/ggerganov/llama.cpp:server

```

### 4. Run the multi-container app

```bash

docker compose up

```

### 5. Go to `localhost:8670` and choose your assistant



You will see something like this:





    Task choice interface




Choose the task among:



- *retrieval-text-generation*: use `qdrant` backend to build a retrieval-friendly knowledge base, which you can query and tune the response of your model on. You have to pass either a pdf/a bunch of pdfs specified as comma-separated paths or a directory where all the pdfs of interest are stored (**DO NOT** provide both); you can also specify the language in which the PDF is written, using [ISO nomenclature](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) - **MULTILINGUAL**

- *agnostic-text-generation*: ChatGPT-like text generation (no retrieval architecture), but supports every text-generation model on HF Hub (as long as your hardware supports it!) - **MULTILINGUAL**

- *text-summarization*: summarize text and pdfs, supports every text-summarization model on HF Hub - **ENGLISH ONLY**

- *image-generation*: stable diffusion, supports every text-to-image model on HF Hub - **MULTILINGUAL**

- *image-generation-pollinations*: stable diffusion, use Pollinations AI API; if you choose 'image-generation-pollinations', you do not need to specify anything else apart from the task - **MULTILINGUAL**

- *image-classification*: classify an image, supports every image-classification model on HF Hub - **ENGLISH ONLY**

- *image-to-text*:  describe an image, supports every image-to-text model on HF Hub - **ENGLISH ONLY**

- *audio-classification*: classify audio files or microphone recordings, supports audio-classification models on HF hub

- *speech-recognition*: transcribe audio files or microphone recordings, supports automatic-speech-recognition models on HF hub.

- *video-generation*: generate video upon text prompt, supports text-to-video models on HF hub - **ENGLISH ONLY**

- *protein-folding*: get the 3D structure of a protein from its amino-acid sequence, using ESM-2 backbone model - **GPU ONLY**

- *autotrain*: fine-tune a model on a specific downstream task with autotrain-advanced, just by specifying you HF username, HF writing token and the path to a yaml config file for the training

- *spaces-api-supabase*: use HF Spaces API in combination with Supabase PostgreSQL databases in order to unleash more powerful LLMs and larger RAG-oriented vector databases - **MULTILINGUAL**

- *llama.cpp-and-qdrant*: same as *retrieval-text-generation*, but uses **llama.cpp** as inference engine, so you MUST NOT specify a model - **MULTILINGUAL**

- *build-your-llm*: Build a customizable chat LLM combining a Qdrant database with your PDFs and the power of Anthropic, OpenAI, Cohere or Groq models: you just need an API key! To build the Qdrant database, have to pass either a pdf/a bunch of pdfs specified as comma-separated paths or a directory where all the pdfs of interest are stored (**DO NOT** provide both); you can also specify the language in which the PDF is written, using [ISO nomenclature](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) - **MULTILINGUAL**, **LANGFUSE INTEGRATION**

- *simply-chatting*: Build a customizable chat LLM with the power of Anthropic, OpenAI, Cohere or Groq models (no RAG pipeline): you just need an API key! - **MULTILINGUAL**, **LANGFUSE INTEGRATION**

- *fal-img2img*: Use [fal.ai](https://fal.ai) ComfyUI API to generate images starting from yur PNG and JPEG images: you just need an API key! You can aklso customize the generation working with prompts and seeds - **ENGLISH ONLY**

- *image-retrieval-search*: search an image database uploading a folder as database input. The folder should have the following structure:



```

./

├── test/

|   ├── label1/

|   └── label2/

└── train/

    ├── label1/

    └── label2/

```



You can query the database starting from your own pictures.



### 6. Go to `localhost:7860` and start using your assistant



Once everything is ready, you can head over to `localhost:7860` and start using your assistant:





    Chat interface